Abstract

This paper describes a system for performing multisession
visual mapping in large-scale environments. Multi-session
mapping considers the problem of combining the results of
multiple Simultaneous Localisation and Mapping (SLAM) missions
performed repeatedly over time in the same environment.
The goal is to robustly combine multiple maps in a common
metrical coordinate system, with consistent estimates of uncertainty.
Our work employs incremental Smoothing and Mapping
(iSAM) as the underlying SLAM state estimator and uses an
improved appearance-based method for detecting loop closures
within single mapping sessions and across multiple sessions. To
stitch together pose graph maps from multiple visual mapping
sessions, we employ spatial separator variables, called anchor
nodes, to link together multiple relative pose graphs. We provide
experimental results for multi-session visual mapping in the MIT
Stata Center, demonstrating key capabilities that will serve as
a foundation for future work in large-scale persistent visual
mapping.